GWAO: a multi-objective hybrid metaheuristic for energy-efficient clustering and routing in wireless sensor networks

Wireless Sensor Networks (WSN) play a vital role in monitoring and gathering real-time data about the physical surroundings by deploying large numbers of small, resource-limited, battery-powered sensors. However, limited battery capacity significantly affects network overage, data integrity and operational lifetime in the network. Cluster-based routing improves energy efficiency by organizing nodes into clusters managed by Cluster Heads (CHs). Nevertheless, optimal CH selection and inter-cluster routing constitute a complex NP-hard optimization problem, and existing methods often suffer from limitations in convergence speed, solution quality, and adaptability to practical deployment scenarios. In this paper, Grey Wolf Ant Colony Optimization (GWAO) has been proposed to provide efficient clustering and routing in WSNs. The proposed metaheuristic hybrid algorithm integrates Grey Wolf Optimization (GWO) to elect the best possible CH based on its optimal CH selection, while Ant Colony Optimization (ACO) to provide energy-efficient multi-hop routing in the network. The integration of these two metaheuristic techniques aims to improve energy utilization, reliability and extend network lifetime. The proposed protocol is implemented by using realistic simulation parameters and compared with existing baseline approaches. In the central Base Station scenario, GWACO achieved an FND of 2700 ± 36 rounds and an LND of 6000 ± 52 rounds. Statistical analysis ( p < 0.05) further confirms the effectiveness and robustness of the proposed framework. The experimental results prove that the proposed algorithm outperforms the existing baseline algorithms with providing significant improvement in energy consumption, network lifetime and routing efficiency in WSNs.